Optimising sub-metre resolution 3D geomorphic change detection over large areas using multitemporal airborne laser scanning with Sentinel-1 InSAR and Sentinel-2 optical observations

Airborne laser scanning (ALS) is widely used in studies of Earth surface change and has potential to inform targeted landscape remediation over large areas. Leveraging this capability requires geomorphic change detection methods that exploit the full 3D information contained in ALS point clouds but remains challenging over large areas (i.e., > 10 km²). We developed a methodology for geomorphic change detection over large areas using multitemporal ALS in a multiscale model-to-model cloud comparison (M3C2) framework adapted for volumetric estimation. Time series Sentinel-2 optical observations were used to isolate persistently-bare areas as candidate sites to co-register the ALS point clouds. Geomorphic stability of those sites was determined from coherence change detection using time series Sentinel-1 InSAR, thereby ensuring only geomorphically-stable sites were used for co-registration. Results showed the Sentinel-based co-registration produced a closer vertical alignment (0.00 ± 0.09 m) between ALS point clouds over stable parts of the landscape, while co-registration using an iterative closest-point algorithm contained bias (0.07 ± 0.10 m). The methodology was used to estimate annual sediment yield for a semi-arid catchment in northeastern Australia and results were compared with long-term field-based stream sediment monitoring. The ALS-based geomorphic change detection estimated 2.58 ± 0.54 t·ha⁻¹·a⁻¹ sediment yield and stream sediment monitoring estimated 1.40 t·ha⁻¹·a⁻¹. These similar estimates indicate multitemporal ALS can produce realistic whole-of-catchment sediment yield estimates in ungauged catchments (i.e., with no stream sediment monitoring) and improves the spatial detail of those estimates. Accurately detecting geomorphic change from multitemporal ALS also required a strategy to manage vegetation-related error due to misclassification of ALS point clouds. Combined identification of fine-scale erosion processes and reliable estimation of catchment-scale erosion rates indicates the proposed methodology provides a valuable tool for planning landscape remediation over large areas.